[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121164-en":3,"doc-seo-121164-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121164,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","A machine learning approach to cardiovascular disease prediction with advanced feature selection","Cardiovascular disease (CVD) is a major global health threat requiring accurate risk assessment to enable timely treatment and efficient use of healthcare resources. This study integrates machine learning models with advanced feature selection to improve both prediction accuracy and model interpretability. By identifying key factors within heterogeneous, high-dimensional datasets, feature selection reduces dimensionality and helps mitigate overfitting. Empirical tuning with the wrapper method yields an AUC-ROC drop from 95.1% to 75.1%, demonstrating a refined predictive pipeline.","A machine learning approach to cardiovascular disease prediction with advanced feature selection  \nAbdikadir Hussein Elmi1, Abdijalil Abdullahi1,2, Mohamed Ali Barre1  \n1Department of Information Technology, Faculty of Computing, SIMAD University, Mogadishu, Somalia 2National Advanced IPv6 Centre, Universiti Sains Malaysia, Penang, Malaysia  \nArticle history:  \nReceived Sep 15, 2023 Revised Nov 7, 2023 Accepted Dec 4, 2023  \nKeywords:  \nCardiovascular diseases Feature selection Filter method Machine learning Wrapper method  \nCorresponding Author:  \nCardiovascular diseases (CVDs) pose a significant global public health challenge, necessitating precise risk assessment for proactive treatment and optimal utilization of healthcare resources. This study employs machine learning algorithms and sophisticated feature selection techniques to enhance the accuracy and comprehensibility of CVD prediction models. While traditional risk assessment tools are valuable, they frequently fail to consider the myriad intricate factors that contribute to the heightened risk of CVD. Our methodology employs machine learning algorithms to analyze diverse healthcare data sources and produce advanced predictive models. The salient feature of this research lies in the meticulous application of advanced feature selection techniques, enabling the identification of pivotal factors within heterogeneous datasets. Optimizing feature selection enhances the interpretability of the model, reduces dimensionality, and improves predictive accuracy. The area under the ROC curve (AUC-ROC) score of the wrapper method model significantly decreased from 95.1% to 75.1% after tuning, based on empirical tests that supported the suggested method. This showcases its capacity as a tool for assessing premature CVD susceptibility and developing tailored healthcare strategies. The study highlights the significance of integrating machine learning with feature selection due to the widespread influence of cardiovascular diseases. Integrating this system has the potential to enhance patient care and optimize the utilization of healthcare resources.  \nThis is an open access article under the CC BY-SA license.  \nAbdijalil Abdullahi  \nDepartment of Information Technology, Faculty of Computing, SIMAD University Warshadaha Streat, Wartanabada, Banadir, Mogadishu, Somalia  \nEmail: [cabdijaliil22@gmail.com](cabdijaliil22@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCardiovascular diseases (CVDs) represent a significant and enduring worldwide health challenge. Accurate risk assessment is necessary in order to provide prompt medical treatments and the effective allocation of healthcare resources. CVDs play a substantial role in the global burden of disease and mortality. Given the escalating incidence of these disorders, there is an escalating need to develop accurate assessment techniques for evaluating the likelihood of their occurrence and to execute preventative strategies to mitigate their effects on individuals and healthcare systems [1], [2] . CVD encompass a broad spectrum of health conditions that impact the heart and blood vessels, encompassing coronary artery disease, heart failure, stroke, and hypertension. Collectively, these diseases are a prominent contributor to global mortality ratesand place substantial economic and healthcare strains on societies. The observation that chronic diseases  \nfrequently manifest without readily apparent symptoms underscores the critical need for early and accurate risk evaluation to facilitate prompt medical interventions [3]–[5] .  \nIn recent times, the emergence of machine learning has presented a potential opportunity to tackle the issues presented by CVDs. Machine learning algorithms have the ability to uncover complex relationships among several risk factors when they are trained on broad and comprehensive healthcare datasets. The utilization of this analytical capability possesses the capacity to generate more refined and personal","cbCaiuWTu2uraxiv","https://ap.wps.com/l/cbCaiuWTu2uraxiv","pdf",628799,1,12,"English","en",105,"# Abstract\n# Introduction\n## Cardiovascular disease risk and early assessment\n## Role of machine learning in CVD prediction\n## Importance of feature selection\n## Limitations of traditional risk scoring\n# Method Focus (Feature Selection and Modeling)","[{\"question\":\"Why is accurate cardiovascular disease risk assessment important in this study?\",\"answer\":\"Accurate risk assessment supports prompt medical treatment and better allocation of healthcare resources, while early evaluation helps address CVDs that may develop without clear symptoms.\"},{\"question\":\"What is the main contribution of the proposed approach?\",\"answer\":\"The approach combines machine learning with advanced feature selection to identify pivotal factors in heterogeneous datasets, improving interpretability and predictive performance.\"},{\"question\":\"How does feature selection affect the machine learning models for CVD prediction?\",\"answer\":\"Feature selection prioritizes informative variables, reduces dimensionality, improves interpretability, and helps reduce overfitting in high-dimensional, noisy healthcare data.\"}]","A machine learning approach to cardiovascular disease prediction with advanced feature selection | 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is accurate cardiovascular disease risk assessment important in this study?","Question",{"text":75,"@type":76},"Accurate risk assessment supports prompt medical treatment and better allocation of healthcare resources, while early evaluation helps address CVDs that may develop without clear symptoms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the main contribution of the proposed approach?",{"text":80,"@type":76},"The approach combines machine learning with advanced feature selection to identify pivotal factors in heterogeneous datasets, improving interpretability and predictive performance.",{"name":82,"@type":73,"acceptedAnswer":83},"How does feature selection affect the machine learning models for CVD prediction?",{"text":84,"@type":76},"Feature selection prioritizes informative variables, reduces dimensionality, improves interpretability, and helps reduce overfitting in high-dimensional, noisy healthcare 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